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About

Yuting Su is a leading researcher in affective computing and multimedia retrieval, with a focus on emotion detection and 3D scene understanding. Her most cited work, "I-GCN: Incremental Graph Convolution Network for Conversation Emotion Detection" (2021, 62 citations), pioneers a novel approach to sentiment analysis by modeling the dynamic, contextual flow of emotions in conversations—a critical advancement for social robots, intelligent voice assistants, and human-computer interaction. This work introduces incremental graph convolution to capture evolving emotional states, setting a new standard for real-time emotion detection. Su also contributes to the emerging field of 2D image-based 3D scene retrieval (2018), enabling intuitive search of 3D datasets from simple 2D scene images—a breakthrough for virtual reality and digital libraries. Her research bridges computer vision and affective computing, with applications in robotics and interactive systems. Su’s work is widely cited for its practical impact on conversational AI and multimedia retrieval, and she continues to advance emotion-aware technologies that enhance human-machine communication.